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Semi-supervised orthogonal discriminant analysis with relative distance : integration with a MOO approach

  • Rakesh Kumar SanodiyaEmail author
  • Sriparna Saha
  • Jimson Mathew
Methodologies and Application
  • 67 Downloads

Abstract

In discriminant analysis, trace ratio is an important criterion for minimizing the between-class similarity and maximizing the within-class similarity, simultaneously. In brief, we address the trace ratio problem associated with many semi-supervised discriminant analysis algorithms as they use the normal Euclidean distances between training data samples. Based on this problem, we propose a new semi-supervised orthogonal discriminant analysis technique with relative distance constraints called SSODARD. Different from the existing semi-supervised dimensionality reduction algorithms, our algorithm is more consistent in propagating the label information from the labeled data to the unlabeled data because of the use of relative distance function instead of normal Euclidean distance function. For finding this appropriate relative distance function, we use pairwise constraints generated from labeled data and satisfy them using Bregman projection. Since the projection is not orthogonal, we require an appropriate subset of constraints. In order to select such a subset of constraints, we further develop a framework called MO-SSODARD, which uses evolutionary algorithm while optimizing various validity indices simultaneously. The experimental results on various datasets show that our proposed approaches are superior than the state-of-the-art discriminant algorithms with respect to various validity indices.

Keywords

Semi-supervised learning Dimension reduction Data clustering Bregman projection Validity indices Trace ratio 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human participants

This study does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Rakesh Kumar Sanodiya
    • 1
    Email author
  • Sriparna Saha
    • 1
  • Jimson Mathew
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology PatnaPatnaIndia

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